A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS
文献类型:期刊论文
作者 | An, Fang Xia1,2,3; Stach, S. M.3; Smail, Ian3; Swinbank, A. M.3; Almaini, O.4; Simpson, C.5; Hartley, W.4; Maltby, D. T.4; Ivison, R. J.6,7; Arumugam, V.6,7 |
刊名 | ASTROPHYSICAL JOURNAL |
出版日期 | 2018-08-01 |
卷号 | 862期号:2页码:18 |
ISSN号 | 0004-637X |
关键词 | observations galaxies: evolution galaxies: formation galaxies: high-redshift galaxies: starburst submillimeter: galaxies |
DOI | 10.3847/1538-4357/aacdaa |
英文摘要 | We describe the application of supervised machine-learning algorithms to identify the likely multiwavelength counterparts to submillimeter sources detected in panoramic, single-dish submillimeter surveys. As a training set, we employ a sample of 695 (S-870 mu m greater than or similar to 1 mJy) submillimeter galaxies (SMGs) with precise identifications from the ALMA follow-up of the SCUBA-2 Cosmology Legacy Survey's UKIDSS-UDS field (AS2UDS). We show that radio emission, near-/mid-infrared colors, photometric redshift, and absolute H-band magnitude are effective predictors that can distinguish SMGs from submillimeter-faint field galaxies. Our combined radio + machinelearning method is able to successfully recover similar to 85% of ALMA-identified SMGs that are detected in at least three bands from the ultraviolet to radio. We confirm the robustness of our method by dividing our training set into independent subsets and using these for training and testing, respectively, as well as applying our method to an independent sample of similar to 100 ALMA-identified SMGs from the ALMA/LABOCA ECDF-South Survey (ALESS). To further test our methodology, we stack the 870 mu m ALMA maps at the positions of those K-band galaxies that are classified as SMG counterparts by the machine learning but do not have a >4.3 sigma ALMA detection. The median peak flux density of these galaxies is S-870 mu m, = (0.61 +/- 0.03) mJy, demonstrating that our method can recover faint and/or diffuse SMGs even when they are below the detection threshold of our ALMA observations. In future, we will apply this method to samples drawn from panoramic single-dish submillimeter surveys that currently lack interferometric follow-up observations to address science questions that can only be tackled with large statistical samples of SMGs. |
WOS关键词 | DEEP-FIELD-SOUTH ; STAR-FORMING GALAXIES ; DEGREE EXTRAGALACTIC SURVEY ; ALMA SPECTROSCOPIC SURVEY ; PARKES SELECTED REGIONS ; NUMBER COUNTS ; HIGH-REDSHIFT ; MU-M ; MIDINFRARED COUNTERPARTS ; BOLOMETER CAMERA |
WOS研究方向 | Astronomy & Astrophysics |
语种 | 英语 |
出版者 | IOP PUBLISHING LTD |
WOS记录号 | WOS:000440045800002 |
源URL | [http://libir.pmo.ac.cn/handle/332002/21611] |
专题 | 中国科学院紫金山天文台 |
通讯作者 | An, Fang Xia |
作者单位 | 1.Chinese Acad Sci, Purple Mt Observ, 8 Yuanhua Rd, Nanjing 210034, Jiangsu, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Univ Durham, Dept Phys, Ctr Extragalact Astron, Durham DH1 3LE, England 4.Univ Nottingham, Sch Phys & Astron, Nottingham NG7 2RD, England 5.Northern Operat Ctr, Gemini Observ, 670 N Aohuku Pl, Hilo, HI 96720 USA 6.European Southern Observ, Karl Schwarzschild Str 2, Garching, Germany 7.Univ Edinburgh, Inst Astron, Royal Observ, Blackford Hill, Edinburgh EH9 3HJ, Midlothian, Scotland 8.Univ Manchester, Oxford Rd, Manchester M13 9PL, Lancs, England 9.Acad Sinica, Inst Astron & Astrophys, 1,Sect 4,Roosevelt Rd, Taipei 10617, Taiwan 10.Univ Hertfordshire, Sch Phys Astron & Math, Ctr Astrophys Res, Hatfield AL10 9AB, Herts, England |
推荐引用方式 GB/T 7714 | An, Fang Xia,Stach, S. M.,Smail, Ian,et al. A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS[J]. ASTROPHYSICAL JOURNAL,2018,862(2):18. |
APA | An, Fang Xia.,Stach, S. M..,Smail, Ian.,Swinbank, A. M..,Almaini, O..,...&Coppin, K. E. K..(2018).A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS.ASTROPHYSICAL JOURNAL,862(2),18. |
MLA | An, Fang Xia,et al."A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS".ASTROPHYSICAL JOURNAL 862.2(2018):18. |
入库方式: OAI收割
来源:紫金山天文台
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